I have been engaged in the study of how variability can arise in neuronal firing. In particular, I have focused on the variability that would be generated through the recurrent connections within a network.[unreadable] [unreadable] [unreadable] Most of the past work on the dynamics of interacting neurons or oscillators have focused on the infinite system size limit where fluctuations due to the connections do not appear. However, many biological and neural networks are large but finite sized. The dynamics of such networks are not well understood. With post doctoral fellow, Hedi Soula, we studied the population dynamics of a finite number of stochastically firing neurons. We were able to analytically deduce statistical properties of the network such as the mean and variance of the firing rate. In particular, we showed for a finite population of neurons, the mean firing rate can be approximated using mean field theory but the variance cannot.[unreadable] [unreadable] [unreadable] Post doctoral fellow Michael Buice and I examined the dynamics of a large but finite size network of globally connected oscillators. The model is the weak coupling limit of a mutually connected network of neurons that have a tendency to synchronize due to the connections. We showed that ideas from the kinetic theory of gases and plasmas could be applied to analyze the fluctuations and correlations due to system size effects. In particular, we showed that finite population size could stabilize the marginal asynchronous mode. This had been an open problem for twenty years.